Targeting MELK improves PD-1 blockade efficiency in cervical cancer via enhancing antitumor immunity.

The balance between T helper 1 (Th1) and T helper 2 (Th2) has a critical function in determining intratumoral immune response and anti-PD-1 immunotherapy. The level of maternal embryonic leucine zipper kinase (MELK) is reported to correlate with infiltration of immune cells in cancers, but the underlying molecular mechanism is not clarified. In the present study, we aimed to elucidate the potential function of MELK in cervical cancer. We found that MELK was upregulated and played an oncogenic role in cervical cancer. MELK overexpression shifted Th1/Th2 balance toward Th2 predisposition in mouse cervical tumors in vivo and naive T cells from human PBMCs in vitro, whereas MELK knockdown exhibited opposite effects. MELK overexpression activated NF-κB signaling and promoted IL-6 secretion by cervical cancer cells. Depletion of IL-6 by neutralization antibodies abrogated the influence of MELK on Th1/Th2 balance. In addition, MELK modulated the antitumor activity of cytotoxic CD8+ T cells in cervical tumors, but depletion of Th2 cells by IL-4 neutralization abrogated this effect. Finally, MELK overexpression conferred tolerance to PD-1 blockade in cervical tumors, whereas targeting MELK by OTSSP167 significantly enhanced PD-1 blockade efficiency. Our data elucidated a novel role of MELK in regulating Th1/Th2 balance and anti-PD-1 immunotherapy in cervical cancer.

Hemodynamics in nutcracker syndrome: implications for diagnosis.

BACKGROUND: Nutcracker syndrome is a disease characterized by complex symptoms, making its diagnosis challenging and often delayed, often resulting in a painful experience for the patients. OBJECTIVE: This study aimed to investigate the pathogenesis of nutcracker syndrome through the perspective of hemodynamics by simulating blood flow with varying compression degrees of the left renal vein. METHODS: 3D patient-specific vascular models of the abdominal aorta, superior mesenteric artery and left renal vein were constructed based on CT images of patients suspected of having nutcracker syndrome. A hemodynamic simulation was then conducted using computational fluid dynamics to identify the correlation between alterations in hemodynamic parameters and varying degrees of compression. RESULTS: The study indicated the presence of an evident gradient in velocity distribution over the left renal vein with relatively high degrees of stenosis (α ≤ 50°), with maximum velocity in the central region of the stenosis. Additionally, when the compression degree of the left renal vein increases, the pressure distribution of the left renal vein presents an increasing number of gradient layers. Furthermore, the wall shear stress shows a correlation with the variation of blood flow velocity, i.e., the increase of wall shear stress correlates with the acceleration of the blood flow velocity. CONCLUSIONS: Using computational fluid dynamics as a non-invasive instrument to obtain the hemodynamic characteristics of nutcracker syndrome is feasible and could provide insights into the pathological mechanisms of the nutcracker syndrome supporting clinicians in diagnosis.

Sulforaphane attenuates glycoprotein VI-mediated platelet mitochondrial dysfunction through up-regulating the cAMP/PKA signaling pathway in vitro and in vivo.

Platelet mitochondrial dysfunction is crucial for platelet activation, atherosclerosis and thrombosis. Sulforaphane (SFN) is a dietary isothiocyanate enriched in cruciferous vegetables and possesses multiple health benefits including cardiovascular protection. This study aims to investigate whether and how SFN modulates platelet mitochondrial dysfunction and hyperactivity in vitro and in vivo. Using a series of platelet functional assays in human platelets in vitro, we found that SFN at physiological concentrations attenuated oxidative stress-dependent platelet mitochondrial dysfunction (loss of mitochondrial membrane potential), apoptosis (cytochrome c release, caspase 3 activation and phosphatidylserine exposure) and activation induced by glycoprotein VI (GPVI) agonists (e.g., collagen and convulxin). Moreover, 12-week supplementation of SFN-enriched broccoli sprout extract (BSE, 0.06% diet) in C57BL/6J mice also attenuated GPVI-induced platelet mitochondrial dysfunction, apoptosis and hyperreactivity in vivo. Mechanistically, these inhibitory effects of SFN treatment and BSE supplementation were mainly mediated by up-regulating the cAMP/PKA pathway though decreasing phosphodiesterase 3A (PDE3A) activity. Thus, through modulating the PDE3A/cAMP/PKA signaling pathway, and attenuating platelet mitochondrial dysfunction and hyperreactivity, SFN may be a potent cardioprotective agent.

An Experimental Study of the Mechanical Properties of Partially Rehabilitated Cable Tunnels.

For buried municipal tunnels-such as cable tunnels and utility tunnels with structural defects-due to the sheltering of the internal pipelines, shelves, and other auxiliary facilities, traditional trenchless rehabilitating methods are not applicable since an intact ring is needed for spraying and lining. In these tunnels, only the exposed area at the crown of the ring can be partly rehabilitated. In this paper, three-edge bearing tests (TEBTs) for partially rehabilitated reinforced concrete (RC) pipe sections are carried out to simulate the case of a municipal tunnel and the effects of different repair materials (cement mortar and epoxy resin) and different dimensional parameters of the liner (lining thickness, lining range) on the partial rehabilitation effect of defective RC pipes are studied. The deforming compatibility of the liner-pipe interface is discussed, and the flexural rigidity of the partially rehabilitated section is calculated. The results show that the load-carrying capacities of partial rehabilitated RC pipes are effectively improved.

Heteroatom-doped hierarchical porous carbon aerogels from chitosan for high performance supercapacitors.

Carbon aerogel with various morphology and structural characteristics were prepared from chitosan by freeze drying-carbonization/activation process. The carbon aerogel obtained without activation (CNS-800) exhibited sheet structure and disordered micropores, N mostly existing in the forms of N-5 and N-6 groups. After phosphoric acid activation (CNSP-800), a 3D-honeycomb structure with long-range ordered pore channels was presented, with N-X being the main nitrogen-containing groups, some P-O/P=O and PC groups were also detected. KOH activation of CNS (CNSK-800) led to the formation of incomplete 3D-mesh structure and some graphite-like structure, which is the most unordered structure attributed to the high intense of KOH activation. All the samples showed micro-mesoporous structure, the specific surface area of CNS-800, CNSk-800 and CNSP-800 were 421, 1215 and 1475 m2/g, respectively. When used as electrode in the three-electrode supercapacitor, the capacitance values of 171, 318 and 416 F/g were obtained at 1 A/g in 1 M H2SO4 electrolyte for CNS-800, CNSK-800 and CNSP-800, with the pseudo capacitance contribution rates of 25.4%, 31.6% and 37.6% respectively. Additionally, the capacitance values were 98, 126 and 168 F/g in the two-electrode system respectively, and their energy densities reached 3.35, 4.69 and 6.36 Wh/kg at a power density of 25 W/kg accordingly.

Process alarm prediction using deep learning and word embedding methods.

Industrial alarm systems play an essential role for the safe management of process operations. With the increase in automation and instrumentation of modern process plants, the number of alarms that the operators manage has also increased significantly. The operators are expected to make critical decisions in the presence of flooding alarms, poorly configured and maintained alarms and many nuisance alarms. In this environment, if the incoming alarms can be correctly predicted before they actually occur, the operators may have a chance to address and possibly avoid abnormal behaviors by taking corrective actions in time. Inspired by the application of deep learning in natural language processing, this paper presents an alarm prediction method based on word embedding and recurrent neural networks to predict the next alarm in a process setting. This represents both a novel approach to alarm management as well as a novel application of natural language processing and deep learning techniques to this problem. The proposed method is applied to an actual case study to demonstrate its performance.